[PDF] -Image Classification- Gray Level Co-Occurrence Matrix (GLCM)



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-Image Classification- Gray Level Co-Occurrence Matrix (GLCM)

2 What is it? A co-occurrence matrix, also referred to as a co- occurrence distribution, is defined over an image to be the distribution of co-occurring values at a given offset



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1 ""what is or is not a cow is for the what is or is not a cow is for the public to decide. public to decide."" --L. WittgensteinL. Wittgenstein --Image ClassificationImage Classification--

Gray Level Co

Gray Level Co--Occurrence MatrixOccurrence Matrix

(GLCM) (GLCM)

Joe HayesJoe Hayes

2

What is it?What is it?

A coA co--occurrence occurrence matrixmatrix, also referred to as a co, also referred to as a co-- occurrence occurrence distributiondistribution, is defined over an , is defined over an imageimage to be the distribution of to be the distribution of coco--occurring values at a occurring values at a given offset given offset OrOr

Represents the distance and angular spatial

Represents the distance and angular spatial

relationship over an image sub relationship over an image sub--region of specific region of specific size. size. What are CoWhat are Co--occurring Values?occurring Values? The GLCM is created from a grayThe GLCM is created from a gray--scale scale image. image. The GLCM is calculates how often a pixel The GLCM is calculates how often a pixel with gray with gray--level (grayscale intensity or level (grayscale intensity or Tone)

Tone) value value

iioccurs either horizontally, occurs either horizontally, vertically, or diagonally to adjacent pixels vertically, or diagonally to adjacent pixels with the value with the value jj.. 3 GLCM directions of AnalysisGLCM directions of Analysis

1. Horizontal (01. Horizontal (0

00

2. Vertical (902. Vertical (90

00

3. Diagonal: 3. Diagonal:

a.) Bottom left to top right ( a.) Bottom left to top right (--4545 00 b.) Top left to bottom right ( b.) Top left to bottom right (--135135 00

Denoted PDenoted P

00 ,,PP 4545
,,PP 9090
, , & P& P

135 135

Respectively.Respectively.

Ex. PEx. P

00 ( ( i i , , j j )) Example of directional AnalysisExample of directional Analysis P P 00 ,,PP 4545
,,PP 9090
, , & P& P

135 135

4 Where Where ii& & j j are the gray level values are the gray level values (tone) in the image. (tone) in the image. This is based in the resolution of the This is based in the resolution of the image (i.e. does the image have 8 gray image (i.e. does the image have 8 gray tones or 256?) tones or 256?)

Example ImageExample Image

(8 Tones) (8 Tones) image Or

Sub-Region

GLCMPP

00 (1,2)(1,2) P P 00 (1,1)(1,1)PP 00 (1,3)(1,3) 5

After you create the After you create the GLCMsGLCMs, you can derive several , you can derive several

statistics from them using the different formulas. statistics from them using the different formulas.

These statistics provide information about the texture of an These statistics provide information about the texture of an

image. image.

ExampleExample

The textures below were run using a 7x7 The textures below were run using a 7x7 window. window. All used the invariant direction, which is All used the invariant direction, which is an average of all four spatial an average of all four spatial arrangements. arrangements. Pixel offset is 1 in all cases. Pixel offset is 1 in all cases. 6

Original ImageGLCM Contrast

Original Image GLCM Homogeneity

7

Sources:Sources:

&http://www.google.com/search?hl a&rls a&rls==org.mozilla:enorg.mozilla:en--

US:official&hs

Our BookOur Book

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